Open AccessJournal Article
Counterfactual reasoning and learning systems: the example of computational advertising
Léon Bottou,Jonas Peters,Joaquin Quiñonero-Candela,Denis X. Charles,D. Max Chickering,Elon Portugaly,Dipankar Ray,Patrice Y. Simard,Ed Snelson +8 more
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This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system and allow both humans and algorithms to select the changes that would have improved the system performance.Abstract:
This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such predictions allow both humans and algorithms to select the changes that would have improved the system performance. This work is illustrated by experiments on the ad placement system associated with the Bing search engine.read more
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Benchmarks for Deep Off-Policy Evaluation
Justin Fu,Mohammad Norouzi,Ofir Nachum,George Tucker,Ziyu Wang,Alexander Novikov,Mengjiao Yang,Michael R. Zhang,Yutian Chen,Aviral Kumar,Cosmin Paduraru,Sergey Levine,Tom Le Paine +12 more
TL;DR: In this article, the authors present a collection of policies that in conjunction with existing offline datasets can be used for benchmarking off-policy evaluation and provide a standardized measure of progress.
Proceedings ArticleDOI
Causal Inference and Counterfactual Reasoning (3hr Tutorial)
Emre Kiciman,Amit Sharma +1 more
TL;DR: This tutorial will introduce participants to concepts in causal inference and counterfactual reasoning, drawing from a broad literature from statistics, social sciences and machine learning, and cover a range of methods suitable for doing causal inference with large-scale online data.
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Triply Robust Off-Policy Evaluation
TL;DR: A robust regression approach to off-policy evaluation (OPE) for contextual bandits is proposed that can be used to augment any existing OPE method that utilizes the direct method, and which proves upper bounds on the resulting bias and variance and derives novel minimax bounds based on robust minimax analysis for covariate shift.
Journal ArticleDOI
A perspective on off-policy evaluation in reinforcement learning
TL;DR: It is argued that the authors must address the evaluation problem before RL can be widely adopted in real-life applications, and RL practitioners often find themselves stuck in a painful dilemma.
References
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Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
MonographDOI
Causality: models, reasoning, and inference
TL;DR: The art and science of cause and effect have been studied in the social sciences for a long time as mentioned in this paper, see, e.g., the theory of inferred causation, causal diagrams and the identification of causal effects.
Journal ArticleDOI
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
TL;DR: This article presents a general class of associative reinforcement learning algorithms for connectionist networks containing stochastic units that are shown to make weight adjustments in a direction that lies along the gradient of expected reinforcement in both immediate-reinforcement tasks and certain limited forms of delayed-reInforcement tasks, and they do this without explicitly computing gradient estimates.
Book
Introduction to Reinforcement Learning
TL;DR: In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning.